assert cls.check_config_paths_exist(config) AssertionError

[Lovemyse1f]

❓ Questions and Help

when i run python -u habitat_baselines/run.py --exp-config habitat_baselines/config/pointnav/ppo_pointnav_example.yaml --run-type train, it happened assert cls.check_config_paths_exist(config) AssertionError.,and when i exchanged the --exp-config yaml ,it also displayed the same problem. But it succeed before, the example i have changed has been recovered according to the file in the github. I can't find problem until now.

~/habitat-lab$ python -u habitat_baselines/run.py --exp-config habitat_baselines/config/pointnav/ppo_pointnav_example.yaml --run-type train, 2020-11-25 14:06:17,302 config: BASE_TASK_CONFIG_PATH: configs/tasks/pointnav.yaml CHECKPOINT_FOLDER: data/new_checkpoints CHECKPOINT_INTERVAL: 50 CMD_TRAILING_OPTS: [] ENV_NAME: NavRLEnv EVAL: SPLIT: val USE_CKPT_CONFIG: True EVAL_CKPT_PATH_DIR: data/new_checkpoints FORCE_BLIND_POLICY: False LOG_FILE: train.log LOG_INTERVAL: 10 NUM_PROCESSES: 1 NUM_UPDATES: 10000 ORBSLAM2: ANGLE_TH: 0.2617993877991494 BETA: 100 CAMERA_HEIGHT: 1.25 DEPTH_DENORM: 10.0 DIST_REACHED_TH: 0.15 DIST_TO_STOP: 0.05 D_OBSTACLE_MAX: 4.0 D_OBSTACLE_MIN: 0.1 H_OBSTACLE_MAX: 1.25 H_OBSTACLE_MIN: 0.375 MAP_CELL_SIZE: 0.1 MAP_SIZE: 40 MIN_PTS_IN_OBSTACLE: 320.0 NEXT_WAYPOINT_TH: 0.5 NUM_ACTIONS: 3 PLANNER_MAX_STEPS: 500 PREPROCESS_MAP: True SLAM_SETTINGS_PATH: habitat_baselines/slambased/data/mp3d3_small1k.yaml SLAM_VOCAB_PATH: habitat_baselines/slambased/data/ORBvoc.txt RL: DDPPO: backbone: resnet50 distrib_backend: GLOO num_recurrent_layers: 2 pretrained: False pretrained_encoder: False pretrained_weights: data/ddppo-models/gibson-2plus-resnet50.pth reset_critic: True rnn_type: LSTM sync_frac: 0.6 train_encoder: True POLICY: OBS_TRANSFORMS: CENTER_CROPPER: HEIGHT: 256 WIDTH: 256 CUBE2EQ: CUBE_LENGTH: 256 HEIGHT: 256 SENSOR_UUIDS: [] WIDTH: 512 ENABLED_TRANSFORMS: () RESIZE_SHORTEST_EDGE: SIZE: 256 name: PointNavBaselinePolicy PPO: clip_param: 0.1 entropy_coef: 0.01 eps: 1e-05 gamma: 0.99 hidden_size: 512 lr: 0.00025 max_grad_norm: 0.5 num_mini_batch: 1 num_steps: 128 ppo_epoch: 4 reward_window_size: 50 tau: 0.95 use_gae: True use_linear_clip_decay: True use_linear_lr_decay: True use_normalized_advantage: True value_loss_coef: 0.5 REWARD_MEASURE: distance_to_goal SLACK_REWARD: -0.01 SUCCESS_MEASURE: spl SUCCESS_REWARD: 10.0 SENSORS: ['RGB_SENSOR', 'DEPTH_SENSOR'] SIMULATOR_GPU_ID: 0 TASK_CONFIG: DATASET: CONTENT_SCENES: ['*'] DATA_PATH: data/datasets/pointnav/habitat-test-scenes/v1/{split}/{split}.json.gz SCENES_DIR: data/scene_datasets SPLIT: train TYPE: PointNav-v1 ENVIRONMENT: ITERATOR_OPTIONS: CYCLE: True GROUP_BY_SCENE: True MAX_SCENE_REPEAT_EPISODES: -1 MAX_SCENE_REPEAT_STEPS: 10000 NUM_EPISODE_SAMPLE: -1 SHUFFLE: True STEP_REPETITION_RANGE: 0.2 MAX_EPISODE_SECONDS: 10000000 MAX_EPISODE_STEPS: 500 PYROBOT: BASE_CONTROLLER: proportional BASE_PLANNER: none BUMP_SENSOR: TYPE: PyRobotBumpSensor DEPTH_SENSOR: CENTER_CROP: False HEIGHT: 480 MAX_DEPTH: 5.0 MIN_DEPTH: 0.0 NORMALIZE_DEPTH: True TYPE: PyRobotDepthSensor WIDTH: 640 LOCOBOT: ACTIONS: ['BASE_ACTIONS', 'CAMERA_ACTIONS'] BASE_ACTIONS: ['go_to_relative', 'go_to_absolute'] CAMERA_ACTIONS: ['set_pan', 'set_tilt', 'set_pan_tilt'] RGB_SENSOR: CENTER_CROP: False HEIGHT: 480 TYPE: PyRobotRGBSensor WIDTH: 640 ROBOT: locobot ROBOTS: ['locobot'] SENSORS: ['RGB_SENSOR', 'DEPTH_SENSOR', 'BUMP_SENSOR'] SEED: 100 SIMULATOR: ACTION_SPACE_CONFIG: v0 AGENTS: ['AGENT_0'] AGENT_0: ANGULAR_ACCELERATION: 12.56 ANGULAR_FRICTION: 1.0 COEFFICIENT_OF_RESTITUTION: 0.0 HEIGHT: 1.5 IS_SET_START_STATE: False LINEAR_ACCELERATION: 20.0 LINEAR_FRICTION: 0.5 MASS: 32.0 RADIUS: 0.1 SENSORS: ['RGB_SENSOR'] START_POSITION: [0, 0, 0] START_ROTATION: [0, 0, 0, 1] DEFAULT_AGENT_ID: 0 DEPTH_SENSOR: HEIGHT: 256 HFOV: 90 MAX_DEPTH: 10.0 MIN_DEPTH: 0.0 NORMALIZE_DEPTH: True ORIENTATION: [0.0, 0.0, 0.0] POSITION: [0, 1.25, 0] TYPE: HabitatSimDepthSensor WIDTH: 256 FORWARD_STEP_SIZE: 0.25 HABITAT_SIM_V0: ALLOW_SLIDING: True ENABLE_PHYSICS: False GPU_DEVICE_ID: 0 GPU_GPU: False PHYSICS_CONFIG_FILE: ./data/default.phys_scene_config.json RGB_SENSOR: HEIGHT: 256 HFOV: 90 ORIENTATION: [0.0, 0.0, 0.0] POSITION: [0, 1.25, 0] TYPE: HabitatSimRGBSensor WIDTH: 256 SCENE: data/scene_datasets/habitat-test-scenes/van-gogh-room.glb SEED: 100 SEMANTIC_SENSOR: HEIGHT: 480 HFOV: 90 ORIENTATION: [0.0, 0.0, 0.0] POSITION: [0, 1.25, 0] TYPE: HabitatSimSemanticSensor WIDTH: 640 TILT_ANGLE: 15 TURN_ANGLE: 10 TYPE: Sim-v0 TASK: ACTIONS: ANSWER: TYPE: AnswerAction LOOK_DOWN: TYPE: LookDownAction LOOK_UP: TYPE: LookUpAction MOVE_FORWARD: TYPE: MoveForwardAction STOP: TYPE: StopAction TELEPORT: TYPE: TeleportAction TURN_LEFT: TYPE: TurnLeftAction TURN_RIGHT: TYPE: TurnRightAction ANSWER_ACCURACY: TYPE: AnswerAccuracy COLLISIONS: TYPE: Collisions COMPASS_SENSOR: TYPE: CompassSensor CORRECT_ANSWER: TYPE: CorrectAnswer DISTANCE_TO_GOAL: DISTANCE_TO: POINT TYPE: DistanceToGoal EPISODE_INFO: TYPE: EpisodeInfo GOAL_SENSOR_UUID: pointgoal_with_gps_compass GPS_SENSOR: DIMENSIONALITY: 2 TYPE: GPSSensor HEADING_SENSOR: TYPE: HeadingSensor IMAGEGOAL_SENSOR: TYPE: ImageGoalSensor INSTRUCTION_SENSOR: TYPE: InstructionSensor INSTRUCTION_SENSOR_UUID: instruction MEASUREMENTS: ['DISTANCE_TO_GOAL', 'SUCCESS', 'SPL'] OBJECTGOAL_SENSOR: GOAL_SPEC: TASK_CATEGORY_ID GOAL_SPEC_MAX_VAL: 50 TYPE: ObjectGoalSensor POINTGOAL_SENSOR: DIMENSIONALITY: 2 GOAL_FORMAT: POLAR TYPE: PointGoalSensor POINTGOAL_WITH_GPS_COMPASS_SENSOR: DIMENSIONALITY: 2 GOAL_FORMAT: POLAR TYPE: PointGoalWithGPSCompassSensor POSSIBLE_ACTIONS: ['STOP', 'MOVE_FORWARD', 'TURN_LEFT', 'TURN_RIGHT'] PROXIMITY_SENSOR: MAX_DETECTION_RADIUS: 2.0 TYPE: ProximitySensor QUESTION_SENSOR: TYPE: QuestionSensor SENSORS: ['POINTGOAL_WITH_GPS_COMPASS_SENSOR'] SOFT_SPL: TYPE: SoftSPL SPL: TYPE: SPL SUCCESS: SUCCESS_DISTANCE: 0.2 TYPE: Success SUCCESS_DISTANCE: 0.2 TOP_DOWN_MAP: DRAW_BORDER: True DRAW_GOAL_AABBS: True DRAW_GOAL_POSITIONS: True DRAW_SHORTEST_PATH: True DRAW_SOURCE: True DRAW_VIEW_POINTS: True FOG_OF_WAR: DRAW: True FOV: 90 VISIBILITY_DIST: 5.0 MAP_PADDING: 3 MAP_RESOLUTION: 1024 MAX_EPISODE_STEPS: 1000 TYPE: TopDownMap TYPE: Nav-v0 TENSORBOARD_DIR: tb TEST_EPISODE_COUNT: 2 TORCH_GPU_ID: 0 TRAINER_NAME: ppo VIDEO_DIR: video_dir VIDEO_OPTION: ['disk', 'tensorboard'] 2020-11-25 14:06:17,302 Initializing dataset PointNav-v1 Traceback (most recent call last): File "habitat_baselines/run.py", line 79, in main() File "habitat_baselines/run.py", line 40, in main run_exp(**vars(args)) File "habitat_baselines/run.py", line 75, in run_exp execute_exp(config, run_type) File "habitat_baselines/run.py", line 58, in execute_exp trainer.train() File "/home/oot/habitat-lab/habitat_baselines/rl/ppo/ppo_trainer.py", line 287, in train self.config, get_env_class(self.config.ENV_NAME) File "/home/oot/habitat-lab/habitat_baselines/utils/env_utils.py", line 59, in construct_envs scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET) File "/home/oot/habitat-lab/habitat/datasets/pointnav/pointnav_dataset.py", line 43, in get_scenes_to_load assert cls.check_config_paths_exist(config) AssertionError

[erikwijmans]

That happens when either the pointnav dataset episodes are not where they should be or when the meshes are not where they should be. You can follow the instructions here: https://github.com/facebookresearch/habitat-lab#data

[Lovemyse1f]

Thank you . I fixed it by checking the dataset path in config which echoed in the terminal.

[Qhijane]

That happens when either the pointnav dataset episodes are not where they should be or when the meshes are not where they should be. You can follow the instructions here: https://github.com/facebookresearch/habitat-lab#data Sorry to bother you. I entered this link through the method you mentioned to learn, but I still haven't found a solution. Can you provide me with a more detailed introduction? I don't quite understand how to choose the path when configuring the environment. Looking forward to your reply, thank you very much. Sorry again for bothering you.